A Graph Representation Composed of Geometrical Components for Household Furniture Detection by Autonomous Mobile Robots

A Graph Representation Composed of Geometrical Components for Household Furniture Detection by Autonomous Mobile Robots

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This study proposes a framework to detect and recognize household furniture using autonomous mobile robots.The proposed methodology is based on the analysis and integration of geometric features extracted over click here 3D point clouds.A relational graph is constructed using those features to model and recognize each piece of furniture.A set of sub-graphs corresponding to different partial views allows matching the robot’s perception with partial furniture models.A reduced set of geometric features is employed: horizontal and vertical planes and the legs of the furniture.

These features are characterized through their properties, such as: height, planarity and area.A fast and linear method for the detection of some geometric features is proposed, which is based ngetikin on histograms of 3D points acquired from an RGB-D camera onboard the robot.Similarity measures for geometric features and graphs are proposed, as well.Our proposal has been validated in home-like environments with two different mobile robotic platforms; and partially on some 3D samples of a database.